Tissue-Specific Enrichment of Lymphoma Risk Loci in Regulatory Elements

Super enhancers as master gene regulators in the pathogenesis of hematologic malignancies

Transcriptional deregulation of multiple oncogenes, tumor suppressors and survival pathways is a cancer cell hallmark. Super enhancers (SE) are long stretches of active enhancers in close linear proximity that ensure extraordinarily high expression levels of key genes associated with cell lineage, function and survival. SE landscape is intrinsically prone to changes and reorganization during the course of normal cell differentiation. This functional plasticity is typically utilized by cancer cells, which remodel their SE landscapes to ensure oncogenic transcriptional reprogramming. Multiple recent studies highlighted structural genetic mechanisms in non-coding regions that create new SE or hijack already existing ones. In addition, alterations in abundance/activity of certain SE-associated proteins or certain viral infections can elicit new super enhancers and trigger SE-driven transcriptional changes. For these reasons, SE profiling emerged as a powerful tool for discovering the core transcriptional regulatory circuits in tumor cells. This, in turn, provides new insights into cancer cell biology, and identifies main nodes of key cellular pathways to be potentially targeted. Since SEs are susceptible to inhibition, their disruption results in exponentially amassing 'butterfly' effect on gene expression and cell function. Moreover, many of SE elements are druggable, opening new therapeutic opportunities. Indeed, SE targeting drugs have been studied preclinically in various hematologic malignancies with promising effects. Herein, we review the unique features of SEs, present different cis- and trans-acting mechanisms through which hematologic tumor cells acquire SEs, and finally, discuss the potential of SE targeting in the therapy of hematologic malignancies.

Identification of 31 loci for mammographic density phenotypes and their associations with breast cancer risk

Mammographic density (MD) phenotypes are strongly associated with breast cancer risk and highly heritable. In this GWAS meta-analysis of 24,192 women, we identify 31 MD loci at P < 5 × 10-8, tripling the number known to 46. Seventeen identified MD loci also are associated with breast cancer risk in an independent meta-analysis (P < 0.05). Mendelian randomization analyses show that genetic estimates of dense area (DA), nondense area (NDA), and percent density (PD) are all significantly associated with breast cancer risk (P < 0.05). Pathway analyses reveal distinct biological processes involving DA, NDA and PD loci. These findings provide additional insights into the genetic basis of MD phenotypes and their associations with breast cancer risk.

Learning gene networks underlying clinical phenotypes using SNP perturbation

Availability of genome sequence, molecular, and clinical phenotype data for large patient cohorts generated by recent technological advances provides an opportunity to dissect the genetic architecture of complex diseases at system level. However, previous analyses of such data have largely focused on the co-localization of SNPs associated with clinical and expression traits, each identified from genome-wide association studies and expression quantitative trait locus mapping. Thus, their description of the molecular mechanisms behind the SNPs influencing clinical phenotypes was limited to the single gene linked to the co-localized SNP. Here we introduce PerturbNet, a statistical framework for learning gene networks that modulate the influence of genetic variants on phenotypes, using genetic variants as naturally occurring perturbation of a biological system. PerturbNet uses a probabilistic graphical model to directly model the cascade of perturbation from genetic variants to the gene network to the phenotype network along with the networks at each layer of the biological system. PerturbNet learns the entire model by solving a single optimization problem with an efficient algorithm that can analyze human genome-wide data within a few hours. PerturbNet inference procedures extract a detailed description of how the gene network modulates the genetic effects on phenotypes. Using simulated and asthma data, we demonstrate that PerturbNet improves statistical power for detecting disease-linked SNPs and identifies gene networks and network modules mediating the SNP effects on traits, providing deeper insights into the underlying molecular mechanisms.

Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks

We examined common variation in asthma risk by conducting a meta-analysis of worldwide asthma genome-wide association studies (23,948 cases, 118,538 controls) from ethnically-diverse populations. We identified five new asthma loci, uncovered two additional novel associations at two known asthma loci, established asthma associations at two loci implicated previously in comorbidity of asthma plus hay fever, and confirmed nine known loci. Investigation of pleiotropy showed large overlaps in genetic variants with autoimmune and inflammatory diseases. Enrichment of asthma risk loci in enhancer marks, especially in immune cells, suggests a major role of these loci in the regulation of immune-related mechanisms.

Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels

Leptin is an adipocyte-secreted hormone, the circulating levels of which correlate closely with overall adiposity. Although rare mutations in the leptin (LEP) gene are well known to cause leptin deficiency and severe obesity, no common loci regulating circulating leptin levels have been uncovered. Therefore, we performed a genome-wide association study (GWAS) of circulating leptin levels from 32,161 individuals and followed up loci reaching P<10(-6) in 19,979 additional individuals. We identify five loci robustly associated (P<5 × 10(-8)) with leptin levels in/near LEP, SLC32A1, GCKR, CCNL1 and FTO. Although the association of the FTO obesity locus with leptin levels is abolished by adjustment for BMI, associations of the four other loci are independent of adiposity. The GCKR locus was found associated with multiple metabolic traits in previous GWAS and the CCNL1 locus with birth weight. Knockdown experiments in mouse adipose tissue explants show convincing evidence for adipogenin, a regulator of adipocyte differentiation, as the novel causal gene in the SLC32A1 locus influencing leptin levels. Our findings provide novel insights into the regulation of leptin production by adipose tissue and open new avenues for examining the influence of variation in leptin levels on adiposity and metabolic health.